{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt#画图模块\n",
    "import matplotlib.dates as mdates#日期格式处理模块\n",
    "from  matplotlib import style#用来自定义图表风格的模块\n",
    "#magic command\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='svg'#提高分辨率\n",
    "#from matplotlib.font_manager import FontProperties 画图自定义字体\n",
    "#from pylab import mpl 画图自定义字体\n",
    "#mpl.rcParams['font.sans-serif']=['SimHei']指定默认字体\n",
    "#mpl.rcParams['font.family']='Times New Roman'指定默认字体\n",
    "#以上四步可以把表格标题改成中文，但最好还是用英文、\n",
    "#mpl.rcParams['font.family']=False解决保存图像是负号'-'显示为方块的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Preclose</th>\n",
       "      <th>Open</th>\n",
       "      <th>Highest</th>\n",
       "      <th>Lowest</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Money</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0500</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>95.7900</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>126000</td>\n",
       "      <td>4.940000e+05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>99.9800</td>\n",
       "      <td>104.3000</td>\n",
       "      <td>104.3900</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>104.3900</td>\n",
       "      <td>19700</td>\n",
       "      <td>8.400000e+04</td>\n",
       "      <td>0.044109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>104.3900</td>\n",
       "      <td>109.0700</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>103.7300</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>2800</td>\n",
       "      <td>1.600000e+04</td>\n",
       "      <td>0.045407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>109.1300</td>\n",
       "      <td>113.5700</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>3200</td>\n",
       "      <td>3.100000e+04</td>\n",
       "      <td>0.049666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>114.5500</td>\n",
       "      <td>120.0900</td>\n",
       "      <td>120.2500</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>120.2500</td>\n",
       "      <td>1500</td>\n",
       "      <td>6.000000e+03</td>\n",
       "      <td>0.049760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-25</th>\n",
       "      <td>2901.9518</td>\n",
       "      <td>2891.8918</td>\n",
       "      <td>2897.7674</td>\n",
       "      <td>2872.8497</td>\n",
       "      <td>2886.7416</td>\n",
       "      <td>27463950000</td>\n",
       "      <td>2.732820e+11</td>\n",
       "      <td>-0.005241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-26</th>\n",
       "      <td>2886.7416</td>\n",
       "      <td>2885.9953</td>\n",
       "      <td>2899.1162</td>\n",
       "      <td>2875.3959</td>\n",
       "      <td>2890.8973</td>\n",
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       "      <td>0.001440</td>\n",
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       "    <tr>\n",
       "      <th>2024-07-29</th>\n",
       "      <td>2890.8973</td>\n",
       "      <td>2889.4726</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-30</th>\n",
       "      <td>2891.8453</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2865.1493</td>\n",
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       "      <td>2.694770e+11</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>2879.2996</td>\n",
       "      <td>2877.5409</td>\n",
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       "      <td>0.020647</td>\n",
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      ],
      "text/plain": [
       "             Preclose       Open    Highest     Lowest      Close  \\\n",
       "Day                                                                 \n",
       "1990-12-19        NaN    96.0500    99.9800    95.7900    99.9800   \n",
       "1990-12-20    99.9800   104.3000   104.3900    99.9800   104.3900   \n",
       "1990-12-21   104.3900   109.0700   109.1300   103.7300   109.1300   \n",
       "1990-12-24   109.1300   113.5700   114.5500   109.1300   114.5500   \n",
       "1990-12-25   114.5500   120.0900   120.2500   114.5500   120.2500   \n",
       "...               ...        ...        ...        ...        ...   \n",
       "2024-07-25  2901.9518  2891.8918  2897.7674  2872.8497  2886.7416   \n",
       "2024-07-26  2886.7416  2885.9953  2899.1162  2875.3959  2890.8973   \n",
       "2024-07-29  2890.8973  2889.4726  2898.9512  2878.5825  2891.8453   \n",
       "2024-07-30  2891.8453  2885.2152  2885.2152  2865.1493  2879.2996   \n",
       "2024-07-31  2879.2996  2877.5409  2940.5927  2876.3009  2938.7493   \n",
       "\n",
       "                 Volume         Money    Return  \n",
       "Day                                              \n",
       "1990-12-19       126000  4.940000e+05       NaN  \n",
       "1990-12-20        19700  8.400000e+04  0.044109  \n",
       "1990-12-21         2800  1.600000e+04  0.045407  \n",
       "1990-12-24         3200  3.100000e+04  0.049666  \n",
       "1990-12-25         1500  6.000000e+03  0.049760  \n",
       "...                 ...           ...       ...  \n",
       "2024-07-25  27463950000  2.732820e+11 -0.005241  \n",
       "2024-07-26  27838753600  2.754430e+11  0.001440  \n",
       "2024-07-29  25689972700  2.600950e+11  0.000328  \n",
       "2024-07-30  26247883700  2.694770e+11 -0.004338  \n",
       "2024-07-31  41272341700  4.188720e+11  0.020647  \n",
       "\n",
       "[8210 rows x 8 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入处理\n",
    "data = pd.read_csv('000001.csv')#datasets/000001.csv运行不了，不知道为什么\n",
    "data['Day']=pd.to_datetime(data['Day'],format='%Y/%m/%d')\n",
    "data.set_index('Day',inplace=True)\n",
    "data['Close']=pd.to_numeric(data['Close'],errors='coerce')\n",
    "data['Preclose']=(data['Close']).shift(1)\n",
    "data['Return']=(data['Close']-data['Preclose'])/data['Preclose']\n",
    "data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monthly Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>-0.131631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-28</th>\n",
       "      <td>-0.023694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
       "      <td>0.177803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-30</th>\n",
       "      <td>-0.103552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>0.207922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>0.008622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-30</th>\n",
       "      <td>0.020932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-31</th>\n",
       "      <td>-0.005801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>-0.038684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>-0.009656</td>\n",
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      ],
      "text/plain": [
       "              Return\n",
       "Day                 \n",
       "1995-01-31 -0.131631\n",
       "1995-02-28 -0.023694\n",
       "1995-03-31  0.177803\n",
       "1995-04-30 -0.103552\n",
       "1995-05-31  0.207922\n",
       "...              ...\n",
       "2024-03-31  0.008622\n",
       "2024-04-30  0.020932\n",
       "2024-05-31 -0.005801\n",
       "2024-06-30 -0.038684\n",
       "2024-07-31 -0.009656\n",
       "\n",
       "[355 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new=data['1995-01-01':'2024-12-31'].copy()\n",
    "Month_data=data_new.resample('ME')['Return'].apply(lambda x:(1+x).prod()-1).to_frame()\n",
    "Month_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画图Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
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   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,4))\n",
    "\n",
    "ax.plot('Return',#图片数据\n",
    "'.-',#图片类型，可以改成*--等，\n",
    "color='r',#图片颜色\n",
    "label='Monthly Return',#图片标签\n",
    "linewidth=1,#图片线宽\n",
    "data=Month_data)#图片数据来源\n",
    "ax.set_title(\"China's Stock Market Monthly Rrturn\")\n",
    "ax.set_ylabel('Return')#图片y轴标签\n",
    "ax.set_xlabel('Year')#图片x轴标签\n",
    "#设置x轴的日期显示格式\n",
    "data_format = mdates.DateFormatter('%Y')\n",
    "ax.xaxis.set_major_formatter(data_format)\n",
    "ax.xaxis.set_major_locator(mdates.YearLocator())\n",
    "#转置x轴的日期显示格式\n",
    "plt.xticks(rotation=90)\n",
    "#添加图例\n",
    "plt.legend(loc='upper right',fontsize= 8)\n",
    "fig.savefig('China_Stock_Market_Monthly_Return.pdf')#保存到特定文件夹到d盘\n",
    "\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args: 'float | ArrayLike | str', scalex: 'bool' = True, scaley: 'bool' = True, data=None, **kwargs) -> 'list[Line2D]'\n",
      "    Plot y versus x as lines and/or markers.\n",
      "\n",
      "    Call signatures::\n",
      "\n",
      "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "\n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "\n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "\n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "\n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "\n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "    ...      linewidth=2, markersize=12)\n",
      "\n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "\n",
      "\n",
      "    **Plotting labelled data**\n",
      "\n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "\n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "\n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFrame` or a structured numpy array.\n",
      "\n",
      "\n",
      "    **Plotting multiple sets of data**\n",
      "\n",
      "    There are various ways to plot multiple sets of data.\n",
      "\n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "\n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "\n",
      "    - If *x* and/or *y* are 2D arrays a separate data set will be drawn\n",
      "      for every column. If both *x* and *y* are 2D, they must have the\n",
      "      same shape. If only one of them is 2D with shape (N, m) the other\n",
      "      must have length N and will be used for every data set m.\n",
      "\n",
      "      Example:\n",
      "\n",
      "      >>> x = [1, 2, 3]\n",
      "      >>> y = np.array([[1, 2], [3, 4], [5, 6]])\n",
      "      >>> plot(x, y)\n",
      "\n",
      "      is equivalent to:\n",
      "\n",
      "      >>> for col in range(y.shape[1]):\n",
      "      ...     plot(x, y[:, col])\n",
      "\n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "\n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "\n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also, this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "\n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using\n",
      "    :rc:`axes.prop_cycle`.\n",
      "\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional and default to ``range(len(y))``.\n",
      "\n",
      "        Commonly, these parameters are 1D arrays.\n",
      "\n",
      "        They can also be scalars, or two-dimensional (in that case, the\n",
      "        columns represent separate data sets).\n",
      "\n",
      "        These arguments cannot be passed as keywords.\n",
      "\n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "\n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "\n",
      "        This argument cannot be passed as keyword.\n",
      "\n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "\n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n",
      "            could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            ``plot('n', 'o', '', data=obj)``.\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    list of `.Line2D`\n",
      "        A list of lines representing the plotted data.\n",
      "\n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, default: True\n",
      "        These parameters determine if the view limits are adapted to the\n",
      "        data limits. The values are passed on to\n",
      "        `~.axes.Axes.autoscale_view`.\n",
      "\n",
      "    **kwargs : `~matplotlib.lines.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "\n",
      "        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n",
      "\n",
      "        If you specify multiple lines with one plot call, the kwargs apply\n",
      "        to all those lines. In case the label object is iterable, each\n",
      "        element is used as labels for each set of data.\n",
      "\n",
      "        Here is a list of available `.Line2D` properties:\n",
      "\n",
      "        Properties:\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image\n",
      "        alpha: scalar or None\n",
      "        animated: bool\n",
      "        antialiased or aa: bool\n",
      "        clip_box: `~matplotlib.transforms.BboxBase` or None\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        color or c: color\n",
      "        dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
      "        dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
      "        dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "        data: (2, N) array or two 1D arrays\n",
      "        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "        figure: `~matplotlib.figure.Figure`\n",
      "        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "        gapcolor: color or None\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "        linewidth or lw: float\n",
      "        marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n",
      "        markeredgecolor or mec: color\n",
      "        markeredgewidth or mew: float\n",
      "        markerfacecolor or mfc: color\n",
      "        markerfacecoloralt or mfcalt: color\n",
      "        markersize or ms: float\n",
      "        markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]\n",
      "        mouseover: bool\n",
      "        path_effects: list of `.AbstractPathEffect`\n",
      "        picker: float or callable[[Artist, Event], tuple[bool, dict]]\n",
      "        pickradius: float\n",
      "        rasterized: bool\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
      "        solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
      "        transform: unknown\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xdata: 1D array\n",
      "        ydata: 1D array\n",
      "        zorder: float\n",
      "\n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of varying size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "\n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "\n",
      "    A format string consists of a part for color, marker and line::\n",
      "\n",
      "        fmt = '[marker][line][color]'\n",
      "\n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "\n",
      "    Other combinations such as ``[color][marker][line]`` are also\n",
      "    supported, but note that their parsing may be ambiguous.\n",
      "\n",
      "    **Markers**\n",
      "\n",
      "    =============   ===============================\n",
      "    character       description\n",
      "    =============   ===============================\n",
      "    ``'.'``         point marker\n",
      "    ``','``         pixel marker\n",
      "    ``'o'``         circle marker\n",
      "    ``'v'``         triangle_down marker\n",
      "    ``'^'``         triangle_up marker\n",
      "    ``'<'``         triangle_left marker\n",
      "    ``'>'``         triangle_right marker\n",
      "    ``'1'``         tri_down marker\n",
      "    ``'2'``         tri_up marker\n",
      "    ``'3'``         tri_left marker\n",
      "    ``'4'``         tri_right marker\n",
      "    ``'8'``         octagon marker\n",
      "    ``'s'``         square marker\n",
      "    ``'p'``         pentagon marker\n",
      "    ``'P'``         plus (filled) marker\n",
      "    ``'*'``         star marker\n",
      "    ``'h'``         hexagon1 marker\n",
      "    ``'H'``         hexagon2 marker\n",
      "    ``'+'``         plus marker\n",
      "    ``'x'``         x marker\n",
      "    ``'X'``         x (filled) marker\n",
      "    ``'D'``         diamond marker\n",
      "    ``'d'``         thin_diamond marker\n",
      "    ``'|'``         vline marker\n",
      "    ``'_'``         hline marker\n",
      "    =============   ===============================\n",
      "\n",
      "    **Line Styles**\n",
      "\n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "\n",
      "    Example format strings::\n",
      "\n",
      "        'b'    # blue markers with default shape\n",
      "        'or'   # red circles\n",
      "        '-g'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        '^k:'  # black triangle_up markers connected by a dotted line\n",
      "\n",
      "    **Colors**\n",
      "\n",
      "    The supported color abbreviations are the single letter codes\n",
      "\n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "\n",
      "    and the ``'CN'`` colors that index into the default property cycle.\n",
      "\n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>-0.226487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>-0.018125</td>\n",
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       "      <th>2016-03-31</th>\n",
       "      <td>0.117538</td>\n",
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       "      <th>2016-04-30</th>\n",
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       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>-0.007385</td>\n",
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       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>0.004454</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-31</th>\n",
       "      <td>0.016975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-31</th>\n",
       "      <td>0.035629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>-0.026184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-31</th>\n",
       "      <td>0.031880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>0.048231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>-0.045043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Return\n",
       "Day                 \n",
       "2016-01-31 -0.226487\n",
       "2016-02-29 -0.018125\n",
       "2016-03-31  0.117538\n",
       "2016-04-30 -0.021838\n",
       "2016-05-31 -0.007385\n",
       "2016-06-30  0.004454\n",
       "2016-07-31  0.016975\n",
       "2016-08-31  0.035629\n",
       "2016-09-30 -0.026184\n",
       "2016-10-31  0.031880\n",
       "2016-11-30  0.048231\n",
       "2016-12-31 -0.045043"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Month_data['2016':'2016']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1069419422.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[55], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    Quarterly_data=data_new.\u001b[0m\n\u001b[1;37m                            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Quarterly_data=data_new."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Yearly_data['1995':'2023']['Return'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('中国股票市场月度收益率的均值',Month_data['2019':'2023']['Return'].mean().round(4)*100,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('中国股票市场收益率年化平均值是：',Yearly_data['1995':'2023']['Return'].mean().round(4)*100,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(Year_data['1995':'2023']['Return'])#把mean写到前面时要在前面加np."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Monthly_data['1995':'2023']['Return'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Quarterly_data['1995':'2023']['Return'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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